Distance Between Two Coordinates, How to Simplify This And/Or Use a Different Technique

Calculate distance between two latitude-longitude points? (Haversine formula)

This link might be helpful to you, as it details the use of the Haversine formula to calculate the distance.

Excerpt:

This script [in Javascript] calculates great-circle distances between the two points –
that is, the shortest distance over the earth’s surface – using the
‘Haversine’ formula.

function getDistanceFromLatLonInKm(lat1,lon1,lat2,lon2) {
  var R = 6371; // Radius of the earth in km
  var dLat = deg2rad(lat2-lat1);  // deg2rad below
  var dLon = deg2rad(lon2-lon1); 
  var a = 
    Math.sin(dLat/2) * Math.sin(dLat/2) +
    Math.cos(deg2rad(lat1)) * Math.cos(deg2rad(lat2)) * 
    Math.sin(dLon/2) * Math.sin(dLon/2)
    ; 
  var c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1-a)); 
  var d = R * c; // Distance in km
  return d;
}

function deg2rad(deg) {
  return deg * (Math.PI/180)
}

Calculate distance between 2 GPS coordinates

Calculate the distance between two coordinates by latitude and longitude, including a Javascript implementation.

West and South locations are negative.
Remember minutes and seconds are out of 60 so S31 30' is -31.50 degrees.

Don't forget to convert degrees to radians. Many languages have this function. Or its a simple calculation: radians = degrees * PI / 180.

function degreesToRadians(degrees) {
  return degrees * Math.PI / 180;
}

function distanceInKmBetweenEarthCoordinates(lat1, lon1, lat2, lon2) {
  var earthRadiusKm = 6371;

  var dLat = degreesToRadians(lat2-lat1);
  var dLon = degreesToRadians(lon2-lon1);

  lat1 = degreesToRadians(lat1);
  lat2 = degreesToRadians(lat2);

  var a = Math.sin(dLat/2) * Math.sin(dLat/2) +
          Math.sin(dLon/2) * Math.sin(dLon/2) * Math.cos(lat1) * Math.cos(lat2); 
  var c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1-a)); 
  return earthRadiusKm * c;
}

Here are some examples of usage:

distanceInKmBetweenEarthCoordinates(0,0,0,0)  // Distance between same 
                                              // points should be 0
0

distanceInKmBetweenEarthCoordinates(51.5, 0, 38.8, -77.1) // From London
                                                          // to Arlington
5918.185064088764

How can I quickly estimate the distance between two (latitude, longitude) points?

The answers to Haversine Formula in Python (Bearing and Distance between two GPS points) provide Python implementations that answer your question.

Using the implementation below I performed 100,000 iterations in less than 1 second on an older laptop. I think for your purposes this should be sufficient. However, you should profile anything before you optimize for performance.

from math import radians, cos, sin, asin, sqrt
def haversine(lon1, lat1, lon2, lat2):
    """
    Calculate the great circle distance between two points 
    on the earth (specified in decimal degrees)
    """
    # convert decimal degrees to radians 
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
    # haversine formula 
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a)) 
    # Radius of earth in kilometers is 6371
    km = 6371* c
    return km

To underestimate haversine(lat1, long1, lat2, long2) * 0.90 or whatever factor you want. I don't see how introducing error to your underestimation is useful.

How do I calculate the distance between two points of latitude and longitude?

CLLocation *location1 = [[CLLocation alloc] initWithLatitude:lat1 longitude:long1];
CLLocation *location2 = [[CLLocation alloc] initWithLatitude:lat2 longitude:long2];
NSLog(@"Distance i meters: %f", [location1 distanceFromLocation:location2]);
[location1 release];
[location2 release];

You also need to add CoreLocation.framework to your project, and add the import statement:

#import <CoreLocation/CoreLocation.h>

Calculating distance between two points, using latitude longitude?

The Java code given by Dommer above gives slightly incorrect results but the small errors add up if you are processing say a GPS track. Here is an implementation of the Haversine method in Java which also takes into account height differences between two points.

/**
 * Calculate distance between two points in latitude and longitude taking
 * into account height difference. If you are not interested in height
 * difference pass 0.0. Uses Haversine method as its base.
 * 
 * lat1, lon1 Start point lat2, lon2 End point el1 Start altitude in meters
 * el2 End altitude in meters
 * @returns Distance in Meters
 */
public static double distance(double lat1, double lat2, double lon1,
        double lon2, double el1, double el2) {

    final int R = 6371; // Radius of the earth

    double latDistance = Math.toRadians(lat2 - lat1);
    double lonDistance = Math.toRadians(lon2 - lon1);
    double a = Math.sin(latDistance / 2) * Math.sin(latDistance / 2)
            + Math.cos(Math.toRadians(lat1)) * Math.cos(Math.toRadians(lat2))
            * Math.sin(lonDistance / 2) * Math.sin(lonDistance / 2);
    double c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));
    double distance = R * c * 1000; // convert to meters

    double height = el1 - el2;

    distance = Math.pow(distance, 2) + Math.pow(height, 2);

    return Math.sqrt(distance);
}

Efficient way of calculating minimum distance between point and multiple faces

Since we're working with a Voronoi tessellation, we can simplify the current algorithm. Given a grid point p, it belongs to the cell of some site q. Take the minimum over each neighboring site r of the distance from p to the plane that is the perpendicular bisector of qr. We don't need to worry whether the closest point s on the plane belongs to the face between q and r; if not, the segment ps intersects some other face of the cell, which is necessarily closer.

Actually it doesn't even matter if we loop r over some sites that are not neighbors. So if you don't have access to a point location subroutine, or it's slow, we can use a fast nearest neighbors algorithm. Given the grid point p, we know that q is the closest site. Find the second closest site r and compute the distance d(p, bisector(qr)) as above. Now we can prune the sites that are too far away from q (for every other site s, we have d(p, bisector(qs)) ≥ d(q, s)/2 − d(p, q), so we can prune s unless d(q, s) ≤ 2 (d(p, bisector(qr)) + d(p, q))) and keep going until we have either considered or pruned every other site. To do pruning in the best possible way requires access to the guts of the nearest neighbor algorithm; I know that it slots right into the best-first depth-first search of a kd-tree or a cover tree.

How to find distance from the latitude and longitude of two locations?

The Haversine formula assumes a spherical earth. However, the shape of the earh is more complex. An oblate spheroid model will give better results.

If such accuracy is needed, you should better use Vincenty inverse formula.
See http://en.wikipedia.org/wiki/Vincenty's_formulae for details. Using it, you can get a 0.5mm accuracy for the spheroid model.

There is no perfect formula, since the real shape of the earth is too complex to be expressed by a formula. Moreover, the shape of earth changes due to climate events (see http://www.nasa.gov/centers/goddard/earthandsun/earthshape.html), and also changes over time due to the rotation of the earth.

You should also note that the method above does not take altitudes into account, and assumes a sea-level oblate spheroid.

Edit 10-Jul-2010: I found out that there are rare situations for which Vincenty inverse formula does not converge to the declared accuracy. A better idea is to use GeographicLib (see http://sourceforge.net/projects/geographiclib/) which is also more accurate.

Find distance between two points using latitude and longitude in mysql

I think your question says you have the city values for the two cities between which you wish to compute the distance.

This query will do the job for you, yielding the distance in km. It uses the spherical cosine law formula.

Notice that you join the table to itself so you can retrieve two coordinate pairs for the computation.

SELECT a.city AS from_city, b.city AS to_city, 
   111.111 *
    DEGREES(ACOS(LEAST(1.0, COS(RADIANS(a.Latitude))
         * COS(RADIANS(b.Latitude))
         * COS(RADIANS(a.Longitude - b.Longitude))
         + SIN(RADIANS(a.Latitude))
         * SIN(RADIANS(b.Latitude))))) AS distance_in_km
  FROM city AS a
  JOIN city AS b ON a.id <> b.id
 WHERE a.city = 3 AND b.city = 7

Notice that the constant 111.1111 is the number of kilometres per degree of latitude, based on the old Napoleonic definition of the metre as one ten-thousandth of the distance from the equator to the pole. That definition is close enough for location-finder work.

If you want statute miles instead of kilometres, use 69.0 instead.

http://sqlfiddle.com/#!9/21e06/412/0

If you're looking for nearby points you may be tempted to use a clause something like this:

   HAVING distance_in_km < 10.0    /* slow ! */
    ORDER BY distance_in_km DESC

That is (as we say near Boston MA USA) wicked slow.

In that case you need to use a bounding box computation. See this writeup about how to do that. http://www.plumislandmedia.net/mysql/haversine-mysql-nearest-loc/

The formula contains a LEAST() function. Why? Because the ACOS() function throws an error if its argument is even slightly greater than 1. When the two points in question are very close together, the expression with the COS() and SIN() computations can sometimes yield a value slightly greater than 1 due to floating-point epsilon (inaccuracy). The LEAST(1.0, dirty-great-expression) call copes with that problem.

There's a better way, a formula by Thaddeus Vincenty. It uses ATAN2() rather than ACOS() so it's less susceptible to epsilon problems.

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Edit 2022 (by Alexio Vay):
As of today the modern solution should be the following short code:

   select ST_Distance_Sphere(
    point(-87.6770458, 41.9631174),
    point(-73.9898293, 40.7628267)) 

Please check out the answer of Naresh Kumar.

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